Comparer des méthodes

	Test de Friedman robuste	ANOVA à mesures répétées robuste
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1990s–2000s	1990s–2000s
Auteur d'origine	Extension of Friedman (1937); robust variants developed by Wilcox and colleagues	Rand R. Wilcox
Type	Robust nonparametric repeated measures comparison	Robust parametric mean comparison
Source fondatrice	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Alias	robust rank-based repeated measures test, trimmed-mean Friedman test, Friedman test with robust estimation, Fried-type robust test	robust within-subjects ANOVA, trimmed-mean repeated measures ANOVA, robust RM-ANOVA, heteroscedastic repeated measures ANOVA
Apparentées	6	6
Résumé	The robust Friedman test is a nonparametric procedure for comparing three or more related (within-subjects) conditions that replaces standard ranking or mean-based summaries with robust location estimates — typically trimmed means or Winsorized statistics — to reduce the influence of outliers and heavy-tailed distributions on the inference.	Robust repeated measures ANOVA tests whether population trimmed means differ across three or more repeated conditions or time points measured on the same subjects. By replacing ordinary means with 20% trimmed means and replacing variances with Winsorized estimates, it maintains acceptable Type I error and power when data are non-normal, skewed, or contain outliers — conditions under which classical repeated measures ANOVA routinely breaks down.